






"""simple docstring"""


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			os.path.abspath(a__ )
						logger.info(F"""Converting TensorFlow checkpoint from {tf_path}""" )
						# Load weights from TF model
						lowerCAmelCase   :List[Any]						=			tf.train.list_variables(a__ )
						lowerCAmelCase   :Any						=			[]
						lowerCAmelCase   :Optional[int]						=			[]
						lowerCAmelCase   :Dict						=			[]
						for full_name, shape in init_vars:
												# logger.info(f"Loading TF weight {name} with shape {shape}")
												lowerCAmelCase   :List[Any]						=			full_name.split('/' )
												if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
																		logger.info(F"""Skipping non-model layer {full_name}""" )
																		continue
												if "optimizer" in full_name:
																		logger.info(F"""Skipping optimization layer {full_name}""" )
																		continue
												if name[0] == "model":
																		# ignore initial 'model'
																		lowerCAmelCase   :List[str]						=			name[1:]
												# figure out how many levels deep the name is
												lowerCAmelCase   :str						=			0
												for _name in name:
																		if _name.startswith('layer_with_weights' ):
																								depth += 1
																		else:
																								break
												layer_depth.append(a__ )
												# read data
												lowerCAmelCase   :str						=			tf.train.load_variable(a__     ,      a__ )
												names.append('/'.join(a__ ) )
												arrays.append(a__ )
						logger.info(F"""Read a total of {len(a__ ):,} layers""" )

						# Sanity check
						if len(set(a__ ) ) != 1:
												raise ValueError(F"""Found layer names with different depths (layer depth {list(set(a__ ) )})""" )
						lowerCAmelCase   :Union[str, Any]						=			list(set(a__ ) )[0]
						if layer_depth != 1:
												raise ValueError(
												    'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
												    ' heads.' )

						# convert layers
						logger.info('Converting weights...' )
						for full_name, array in zip(a__     ,      a__ ):
												lowerCAmelCase   :str						=			full_name.split('/' )
												lowerCAmelCase   :Optional[Any]						=			model
												lowerCAmelCase   :Optional[Any]						=			[]
												for i, m_name in enumerate(a__ ):
																		if m_name == ".ATTRIBUTES":
																								# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																								break
																		if m_name.startswith('layer_with_weights' ):
																								lowerCAmelCase   :Tuple						=			int(m_name.split('-' )[-1] )
																								if layer_num <= 2:
																														# embedding layers
																														# layer_num 0: word_embeddings
																														# layer_num 1: position_embeddings
																														# layer_num 2: token_type_embeddings
																														continue
																								elif layer_num == 3:
																														# embedding LayerNorm
																														trace.extend(['embeddings', 'LayerNorm'] )
																														lowerCAmelCase   :Tuple						=			getattr(a__     ,      'embeddings' )
																														lowerCAmelCase   :List[str]						=			getattr(a__     ,      'LayerNorm' )
																								elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																														# encoder layers
																														trace.extend(['encoder', 'layer', str(layer_num - 4 )] )
																														lowerCAmelCase   :Optional[Any]						=			getattr(a__     ,      'encoder' )
																														lowerCAmelCase   :Optional[int]						=			getattr(a__     ,      'layer' )
																														lowerCAmelCase   :Dict						=			pointer[layer_num - 4]
																								elif layer_num == config.num_hidden_layers + 4:
																														# pooler layer
																														trace.extend(['pooler', 'dense'] )
																														lowerCAmelCase   :str						=			getattr(a__     ,      'pooler' )
																														lowerCAmelCase   :Dict						=			getattr(a__     ,      'dense' )
																		elif m_name == "embeddings":
																								trace.append('embeddings' )
																								lowerCAmelCase   :Union[str, Any]						=			getattr(a__     ,      'embeddings' )
																								if layer_num == 0:
																														trace.append('word_embeddings' )
																														lowerCAmelCase   :Dict						=			getattr(a__     ,      'word_embeddings' )
																								elif layer_num == 1:
																														trace.append('position_embeddings' )
																														lowerCAmelCase   :List[Any]						=			getattr(a__     ,      'position_embeddings' )
																								elif layer_num == 2:
																														trace.append('token_type_embeddings' )
																														lowerCAmelCase   :Optional[Any]						=			getattr(a__     ,      'token_type_embeddings' )
																								else:
																														raise ValueError(F"""Unknown embedding layer with name {full_name}""" )
																								trace.append('weight' )
																								lowerCAmelCase   :List[Any]						=			getattr(a__     ,      'weight' )
																		elif m_name == "_attention_layer":
																								# self-attention layer
																								trace.extend(['attention', 'self'] )
																								lowerCAmelCase   :List[str]						=			getattr(a__     ,      'attention' )
																								lowerCAmelCase   :List[Any]						=			getattr(a__     ,      'self' )
																		elif m_name == "_attention_layer_norm":
																								# output attention norm
																								trace.extend(['attention', 'output', 'LayerNorm'] )
																								lowerCAmelCase   :Dict						=			getattr(a__     ,      'attention' )
																								lowerCAmelCase   :List[Any]						=			getattr(a__     ,      'output' )
																								lowerCAmelCase   :Tuple						=			getattr(a__     ,      'LayerNorm' )
																		elif m_name == "_attention_output_dense":
																								# output attention dense
																								trace.extend(['attention', 'output', 'dense'] )
																								lowerCAmelCase   :int						=			getattr(a__     ,      'attention' )
																								lowerCAmelCase   :str						=			getattr(a__     ,      'output' )
																								lowerCAmelCase   :Union[str, Any]						=			getattr(a__     ,      'dense' )
																		elif m_name == "_output_dense":
																								# output dense
																								trace.extend(['output', 'dense'] )
																								lowerCAmelCase   :Optional[Any]						=			getattr(a__     ,      'output' )
																								lowerCAmelCase   :str						=			getattr(a__     ,      'dense' )
																		elif m_name == "_output_layer_norm":
																								# output dense
																								trace.extend(['output', 'LayerNorm'] )
																								lowerCAmelCase   :int						=			getattr(a__     ,      'output' )
																								lowerCAmelCase   :Optional[Any]						=			getattr(a__     ,      'LayerNorm' )
																		elif m_name == "_key_dense":
																								# attention key
																								trace.append('key' )
																								lowerCAmelCase   :Tuple						=			getattr(a__     ,      'key' )
																		elif m_name == "_query_dense":
																								# attention query
																								trace.append('query' )
																								lowerCAmelCase   :Optional[int]						=			getattr(a__     ,      'query' )
																		elif m_name == "_value_dense":
																								# attention value
																								trace.append('value' )
																								lowerCAmelCase   :Optional[Any]						=			getattr(a__     ,      'value' )
																		elif m_name == "_intermediate_dense":
																								# attention intermediate dense
																								trace.extend(['intermediate', 'dense'] )
																								lowerCAmelCase   :List[Any]						=			getattr(a__     ,      'intermediate' )
																								lowerCAmelCase   :List[str]						=			getattr(a__     ,      'dense' )
																		elif m_name == "_output_layer_norm":
																								# output layer norm
																								trace.append('output' )
																								lowerCAmelCase   :Optional[int]						=			getattr(a__     ,      'output' )
																		# weights & biases
																		elif m_name in ["bias", "beta"]:
																								trace.append('bias' )
																								lowerCAmelCase   :List[str]						=			getattr(a__     ,      'bias' )
																		elif m_name in ["kernel", "gamma"]:
																								trace.append('weight' )
																								lowerCAmelCase   :Tuple						=			getattr(a__     ,      'weight' )
																		else:
																								logger.warning(F"""Ignored {m_name}""" )
        # for certain layers reshape is necessary
												lowerCAmelCase   :Optional[Any]						=			'.'.join(a__ )
												if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'     ,      a__ ) or re.match(
												    R'(\S+)\.attention\.output\.dense\.weight'     ,      a__ ):
																		lowerCAmelCase   :Optional[int]						=			array.reshape(pointer.data.shape )
												if "kernel" in full_name:
																		lowerCAmelCase   :Optional[Any]						=			array.transpose()
												if pointer.shape == array.shape:
																		lowerCAmelCase   :Tuple						=			torch.from_numpy(a__ )
												else:
																		raise ValueError(
																		    F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
																		    F""" {array.shape}""" )
												logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}""" )
						return model


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						logger.info(F"""Loading model based on config from {config_path}...""" )
						lowerCAmelCase   :Union[str, Any]						=			BertConfig.from_json_file(a__ )
						lowerCAmelCase   :Optional[int]						=			BertModel(a__ )

						# Load weights from checkpoint
						logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}...""" )
						load_tfa_weights_in_bert(a__     ,      a__     ,      a__ )

						# Save pytorch-model
						logger.info(F"""Saving PyTorch model to {pytorch_dump_path}...""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model (must include filename).',
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"